Representation Learning
In this reading assignment, we extend our focus on probablistic models with intractable posterior distributions.
Please read Deep Learning Book - Chapter 19: Approximate Inference for an overview on possible approaches. You can skip most of Section 19.4.1 as indicated in the text.
Then continue reading Deep Learning Book - Chapter 20: Deep Generative Models:
20.3 Deep Belief Networks
20.4 Deep Boltzmann Machines
(This is the most important section.)
Section 20.5 is optional and will not be covered in the course.
Deadline for questions to be considered in class is January 21, 7am. I will also try to accommodate things that come in later but I cannot make guarantees. The earlier you bring up questions, the better.
Like last week, we will spend about 50% of the time in class for discussing the reading assignments. This will leave less time for the course project. Therefore, if you would like to present and/or discuss progress on a specific aspect of the project, please prepare accordingly. Also, please send an email with the topic and the approximate amount of time required until January 23, 10am.